Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines

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Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines. / Munera, Sandra; Blasco, José; Amigo, Jose M.; Cubero, Sergio; Talens, Pau; Aleixos, Nuria.

In: Biosystems Engineering, Vol. 182, 2019, p. 54-64.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Munera, S, Blasco, J, Amigo, JM, Cubero, S, Talens, P & Aleixos, N 2019, 'Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines', Biosystems Engineering, vol. 182, pp. 54-64. https://doi.org/10.1016/j.biosystemseng.2019.04.001

APA

Munera, S., Blasco, J., Amigo, J. M., Cubero, S., Talens, P., & Aleixos, N. (2019). Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines. Biosystems Engineering, 182, 54-64. https://doi.org/10.1016/j.biosystemseng.2019.04.001

Vancouver

Munera S, Blasco J, Amigo JM, Cubero S, Talens P, Aleixos N. Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines. Biosystems Engineering. 2019;182:54-64. https://doi.org/10.1016/j.biosystemseng.2019.04.001

Author

Munera, Sandra ; Blasco, José ; Amigo, Jose M. ; Cubero, Sergio ; Talens, Pau ; Aleixos, Nuria. / Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines. In: Biosystems Engineering. 2019 ; Vol. 182. pp. 54-64.

Bibtex

@article{0d5e47bc505d43efac57ae605ed3be17,
title = "Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines",
abstract = "The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. ‘Big Top’ (yellow flesh) and ‘Magique’ (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630–900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7{\%} was obtained in the detection of fruits with split pit of the ‘Big Top’ cultivar. Accuracies of 95.7{\%} and 94.6{\%} were achieved in the classification of the ‘Big Top’ and ‘Magique’ cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R 2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.",
keywords = "Computer vision, Hyperspectral imaging, Internal quality, Ripeness, Split pit, Stone fruit",
author = "Sandra Munera and Jos{\'e} Blasco and Amigo, {Jose M.} and Sergio Cubero and Pau Talens and Nuria Aleixos",
year = "2019",
doi = "10.1016/j.biosystemseng.2019.04.001",
language = "English",
volume = "182",
pages = "54--64",
journal = "Biosystems Engineering",
issn = "1537-5110",
publisher = "Academic Press",

}

RIS

TY - JOUR

T1 - Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines

AU - Munera, Sandra

AU - Blasco, José

AU - Amigo, Jose M.

AU - Cubero, Sergio

AU - Talens, Pau

AU - Aleixos, Nuria

PY - 2019

Y1 - 2019

N2 - The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. ‘Big Top’ (yellow flesh) and ‘Magique’ (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630–900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split pit of the ‘Big Top’ cultivar. Accuracies of 95.7% and 94.6% were achieved in the classification of the ‘Big Top’ and ‘Magique’ cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R 2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.

AB - The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. ‘Big Top’ (yellow flesh) and ‘Magique’ (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630–900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split pit of the ‘Big Top’ cultivar. Accuracies of 95.7% and 94.6% were achieved in the classification of the ‘Big Top’ and ‘Magique’ cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R 2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.

KW - Computer vision

KW - Hyperspectral imaging

KW - Internal quality

KW - Ripeness

KW - Split pit

KW - Stone fruit

U2 - 10.1016/j.biosystemseng.2019.04.001

DO - 10.1016/j.biosystemseng.2019.04.001

M3 - Journal article

VL - 182

SP - 54

EP - 64

JO - Biosystems Engineering

JF - Biosystems Engineering

SN - 1537-5110

ER -

ID: 217996301